In recent years, popularity and usage of on-demand transportation matching systems have significantly grown. Indeed, the proliferation of web and mobile applications easily enable requesting individuals to request transportation from one geographic area to another geographic area. For instance, an on-demand transportation matching system receives transportation requests and pairs the requests with providers that can transport requesting individuals to the destination locations. In addition, the on-demand transportation matching system provides tools to providers to pick up requesting individuals as well as transport them to the destination locations.
Some existing on-demand transportation matching systems manage networks of hundreds of thousands of providers as well as millions of requesting individuals. Furthermore, within a short time period, these on-demand transportation matching systems are often required to process thousands of transportation requests for a geographic area, where each request involves matching a requesting individual to a provider. Because of the significant technical complexity required to process such large volumes of transportation requests—often occurring at unexpected times—on-demand transportation matching systems often suffer from computational problems and inefficiencies.
As an initial problem, when a large number of transportation requests are received in a short timeframe within a region, these on-demand transportation matching systems commonly struggle to appropriately match the transportation requests because there are not enough providers within a satisfactory distance of the requesting individuals. Indeed, it is typical for the number of received transportation requests for one area to exceed the number of available providers, while at the same time, another area may have a surplus of available providers to the number of transportation requests. This results in delays in processing and matching each transportation request, which thereby increases the number of transportation requests that a transportation matching system handles at any particular time, thereby increasing the computational strain on the transportation matching system.
Some on-demand transportation matching systems have attempted to better allocate available providers across multiple areas to better match the demand of each area. However, these on-demand transportation matching systems have failed to identify an efficient approach that accurately redirects available providers to areas where transportation requests are anticipated. Indeed, these on-demand transportation matching systems employ approaches that are rigid, overly complicated, and computationally inefficient, which prohibits operation in near-real-time. Thus, because these on-demand transportation matching systems rely on outdated information, too often, these systems send providers to areas where they are not needed, wasting the computational resources of the system as well as the time of the providers.
These along with additional problems and issues exist with conventional transportation matching systems.